For safety-related applications, it is crucial to produce trustworthy deep neural networks whose prediction is associated with confidence that can represent the likelihood of correctness for subsequent decision-making. Existing dense binary classification models are prone to being over-confident. To improve model calibration, we propose Adaptive Stochastic Label Perturbation (ASLP) which learns a unique label perturbation level for each training image. ASLP employs our proposed Self-Calibrating Binary Cross Entropy (SC-BCE) loss, which unifies label perturbation processes including stochastic approaches (like DisturbLabel), and label smoothing, to correct calibration while maintaining classification rates. ASLP follows Maximum Entropy Inference of classic statistical mechanics to maximise prediction entropy with respect to missing information. It performs this while: (1) preserving classification accuracy on known data as a conservative solution, or (2) specifically improves model calibration degree by minimising the gap between the prediction accuracy and expected confidence of the target training label. Extensive results demonstrate that ASLP can significantly improve calibration degrees of dense binary classification models on both in-distribution and out-of-distribution data. The code is available on https://github.com/Carlisle-Liu/ASLP.

提出自适应随机标签扰动 (ASLP) 方法，通过学习每个训练图像的独特标签扰动水平，使用自校准二值交叉熵（SC-BCE）损失统一标签扰动过程，最大化预测熵，并通过最小化预测准确度和目标训练标签的预期置信度之间的差距来改善模型校准度。实验结果表明，ASLP可以显著提高密集二值分类模型在分布内和分布外数据上的校准度。

自适应标签扰动下的密集分类模型校准